A machine learning-based risk prediction model of chronic obstructive pulmonary disease with lung cancer_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

LI Mengqi ¹ , HE Fengtai ² , WU Yanan ³ ,  ZHANG Jinhui ¹ ,  WANG Qi ¹

1. Department of Respiratory Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116023, P. R. China;
2. Radiology Department, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116023, P. R. China;
3. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110819, P. R. China;

Corresponding?author：

ZHANG Jinhui, Email: wqdlmu@163.com; WANG Qi, Email: zjhdyey@163.com

Keywords：

Lung cancer; Chronic obstructive pulmonary disease; Convolutional neural network; Machine learning

DOI：

10.7507/1671-6205.202111050

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To establish a machine learning-based risk prediction model of combined chronic obstructive pulmonary disease (COPD) with lung cancer, so as to explore the high risk factors for COPD patients with lung cancer and to lay the foundation for early detection of lung cancer risk in COPD patients. Methods A total of 154 patients from the Second Hospital of Dalian Medical University from 2010 to 2021 were retrospectively analyzed, including 99 patients in the COPD group and 55 patients in the COPD with lung cancer group. the chest high resolution computed tomography (HRCT) scans and pulmonary function test of each patient were acquired. The main analyses were as follow: (1) to valid the statistically differences of the basic information (such as age, body mass index, smoking index), laboratory test results, pulmonary function parameters and quantitative parameters of chest HRCT between the two groups; (2) to analyze the indicators of high risk factors for lung cancer in COPD patients using univariate and binary logistic regression (LR) methods; and (3) to establish the machine learning model (such as LR and Gaussian process) for COPD with lung cancer patients. Results Based on the statistical analysis and LR methods, decreased BMI, increased whole lung emphysema index, increased whole lung mean density, and increased percentage activity of exertional spirometry and prothrombin time were risk factors for COPD with lung cancer patients. Based on the machine learning prediction model for COPD with lung cancer patients, the area under the receiver operating characteristic curve for LR and Gaussian process were obtained as 0.88 using the soluble fragments of prothrombin time percentage activity, whole lung emphysema index, whole lung mean density, and forced vital capacity combined with neuron-specific enolase and cytokeratin 19 as features. Conclusion The prediction model of COPD with lung cancer patients using a machine learning approach can be used for early detection of lung cancer risk in COPD patients.

Citation： LI Mengqi, HE Fengtai, WU Yanan, ZHANG Jinhui, WANG Qi. A machine learning-based risk prediction model of chronic obstructive pulmonary disease with lung cancer. Chinese Journal of Respiratory and Critical Care Medicine, 2022, 21(11): 782-789. doi: 10.7507/1671-6205.202111050 Copy

1.	National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
2.	Mets OM, Schmidt M, Buckens CF, et al. Diagnosis of chronic obstructive pulmonary disease in lung cancer screening computed tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening. Respir Res, 2013, 14(1): 59.
3.	Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
4.	Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2): 84-90.
5.	Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017, 2017: 8314740.
6.	李夢琪, 王琪, 李恩成, 等. 基于大數據的肺癌合并COPD患者的臨床特征. 西北國防醫學雜志., 2021, 42(5): 285-290.
7.	Sogancioglu E, Murphy K, Th Scholten E, et al. Automated estimation of total lung volume using chest radiographs and deep learning. Med Phys, 2022, 49(7): 4466-4477.
8.	Global Inititative for Chronic Obstructive Lung Disease. Global Strategy For The Diagnosis, Management and Prevention of Chronic Obstuctive Pulmonary disease. 2022 Report. www. goldcopd. org.
9.	中華醫學會, 中華醫學會腫瘤學分會, 中華醫學會雜志社. 中華醫學會肺癌臨床診療指南(2019版). 中華腫瘤雜志, 2020, 42(4): 257-287.
10.	中華醫學會呼吸病學分會肺功能專業組. 肺功能檢查指南(第二部分)—肺量計檢查. 中華結核和呼吸雜志, 2014, 37(7): 481-486.
11.	Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
12.	de-Torres JP, Marín JM, Casanova C, et al. Identification of COPD patients at high risk for lung cancer mortality using the COPD-LUCSS-DLCO. Chest, 2016, 149(4): 936-942.
13.	Bishawi M, Moore W, Bilfinger T. Severity of emphysema predicts location of lung cancer and 5-y survival of patients with stage I non-small cell lung cancer. J Surg Res, 2013, 184(1): 1-5.
14.	Carr LL, Jacobson S, Lynch DA, et al. Features of COPD as predictors of lung cancer. Chest, 2018, 153(6): 1326-1335.
15.	Tammem?gi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med, 2013, 368(8): 728-736.
16.	Katki HA, Kovalchik SA, Petito LC, et al. Implications of nine risk prediction models for selecting ever-smokers for computed tomography lung cancer screening. Ann Intern Med, 2018, 169(1): 10-19.
17.	Kim KH, Park TY, Lee JY, et al. Prognostic significance of initial platelet counts and fibrinogen level in advanced non-small cell lung cancer. J Korean Med Sci, 2014, 29(4): 507-511.
18.	Labaki WW, Xia M, Murray S, et al. Quantitative emphysema on low-dose CT imaging of the chest and risk of lung cancer and airflow obstruction: an analysis of the National Lung Screening Trial. Chest, 2021, 159(5): 1812-1820.
19.	Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer, Guida F, Sun N, Bantis LE, et al. Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. JAMA Oncol, 2018, 4(10): e182078.

1. National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
2. Mets OM, Schmidt M, Buckens CF, et al. Diagnosis of chronic obstructive pulmonary disease in lung cancer screening computed tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening. Respir Res, 2013, 14(1): 59.
3. Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
4. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2): 84-90.
5. Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017, 2017: 8314740.
6. 李夢琪, 王琪, 李恩成, 等. 基于大數據的肺癌合并COPD患者的臨床特征. 西北國防醫學雜志., 2021, 42(5): 285-290.
7. Sogancioglu E, Murphy K, Th Scholten E, et al. Automated estimation of total lung volume using chest radiographs and deep learning. Med Phys, 2022, 49(7): 4466-4477.
8. Global Inititative for Chronic Obstructive Lung Disease. Global Strategy For The Diagnosis, Management and Prevention of Chronic Obstuctive Pulmonary disease. 2022 Report. www. goldcopd. org.
9. 中華醫學會, 中華醫學會腫瘤學分會, 中華醫學會雜志社. 中華醫學會肺癌臨床診療指南(2019版). 中華腫瘤雜志, 2020, 42(4): 257-287.
10. 中華醫學會呼吸病學分會肺功能專業組. 肺功能檢查指南(第二部分)—肺量計檢查. 中華結核和呼吸雜志, 2014, 37(7): 481-486.
11. Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods, 2021, 18(2): 203-211.
12. de-Torres JP, Marín JM, Casanova C, et al. Identification of COPD patients at high risk for lung cancer mortality using the COPD-LUCSS-DLCO. Chest, 2016, 149(4): 936-942.
13. Bishawi M, Moore W, Bilfinger T. Severity of emphysema predicts location of lung cancer and 5-y survival of patients with stage I non-small cell lung cancer. J Surg Res, 2013, 184(1): 1-5.
14. Carr LL, Jacobson S, Lynch DA, et al. Features of COPD as predictors of lung cancer. Chest, 2018, 153(6): 1326-1335.
15. Tammem?gi MC, Katki HA, Hocking WG, et al. Selection criteria for lung-cancer screening. N Engl J Med, 2013, 368(8): 728-736.
16. Katki HA, Kovalchik SA, Petito LC, et al. Implications of nine risk prediction models for selecting ever-smokers for computed tomography lung cancer screening. Ann Intern Med, 2018, 169(1): 10-19.
17. Kim KH, Park TY, Lee JY, et al. Prognostic significance of initial platelet counts and fibrinogen level in advanced non-small cell lung cancer. J Korean Med Sci, 2014, 29(4): 507-511.
18. Labaki WW, Xia M, Murray S, et al. Quantitative emphysema on low-dose CT imaging of the chest and risk of lung cancer and airflow obstruction: an analysis of the National Lung Screening Trial. Chest, 2021, 159(5): 1812-1820.
19. Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer, Guida F, Sun N, Bantis LE, et al. Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. JAMA Oncol, 2018, 4(10): e182078.

Chinese Journal of Respiratory and Critical Care Medicine

A machine learning-based risk prediction model of chronic obstructive pulmonary disease with lung cancer

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content